similar to: Ridge regression and mixed models

Hello! If I run this example: library(nlme) fm1 <- lme(distance ~ age+Sex, Orthodont, random = ~ age + Sex| Subject) If I run: summary(fm1) then I can see the fixed effects for age and sex (17.7 for intercept, 0.66 for age, and -1.66 for SexFemale) If I run: ranef(fm1) Then it looks like it's producing the random effects for each subgroup (in this example - each subject). For example,

Dear R-list, I?m trying to use the lmer of the lme4 package to fit a linear mixed model of the form Y = Xb + Zu + e and I can?t figure out how to control the covariance structure of u. I want u ~ N(0,sigma^2*I). More precisely I?m trying to smooth a curve through data using the "Penalized Splines as BLUPs" method as described in Ruppert, Wand & Carroll (2003). So I have Z = [Z1

Hello, I have questions regarding penalized Cox regression using survival package (functions coxph() and ridge()). I am using R 2.8.0 on Ubuntu Linux and survival package version 2.35-4. Question 1. Consider the following example from help(ridge): > fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1), ovarian) As I understand, this builds a model in which `rx' is

Dear all, I am using "penalized" package for "Ridge" regression. I do not know how can I get regression coefficients using that package . Please help me. Thanks -- Linda Garcia [[alternative HTML version deleted]]

It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected? In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.

Dear all, I need to calculate likelihood ratio test for ridge regression. In February I have reported a bug where coxph returns unpenalized log-likelihood for final beta estimates for ridge coxph regression. In high-dimensional settings ridge regression models usually fail for lower values of lambda. As the result of it, in such settings the ridge regressions have higher values of lambda (e.g.

I am trying to fit a large Cox model with many predictors. Because there are many predictors, I would like to use the ridge() function to get penalized ml estimates for all coefficients. The problems are that: 1. When I include a factor (like race) in the ridge() function, dummy variables are not created. The resulting model has a single coefficient for the race variable, and I have

Dear everyone I'm dealing with a problem related to Poisson Ridge Regression. If anyone can help me in this regard by telling if any changes in the source code of "glm.fit" may help -- Regards Umesh Khatri

Hello R-user I want to compute a multiple regression but I would to include a check for collinearity of the variables. Therefore I would like to use a ridge regression. I tried lm.ridge() but I don't know yet how to get p-values (single Pr() and p of the whole model) out of this model. Can anybody tell me how to get a similar output like the summary(lm(...)) output? Or if there is

Hello! I wrote a function that returns a data frame. Nowhere in the function do I say print(my.data.frame), but when I run the function - the data frame is printed on the console. Is there any way to suppress it? Thank you! -- Dimitri Liakhovitski Ninah Consulting www.ninah.com

Hello! I am wondering if it's possible to run - in sequence - code that is stored in several R scripts. For example: Script in the file "code1.r" contains the code: a = 3; b = 5; c = a + b Script in the file "code2.r" contains the code: d = 10; e = d - c Script in the file "code3.r" contains the code: result=e/a I understand that I could write those 3 scripts

Hello! s<-"start"; e<-"end" middle<-as.character(c(1,2,3)) I would like to get the following result: "start 123 end" or "start 1 2 3 end" or "start 1,2,3 end" How can I avoide this (undesired) result: paste(s,middle,e,sep=" ") Thank you! -- Dimitri Liakhovitski Ninah Consulting www.ninah.com

Dear R-ers! I have x as a variable in a data frame x. x<-data.frame(x=c("aa.bb","cc.dd.ee")) x$x<-as.character(x$x) x I am sorry for such a simple question - but how can I replace all periods in x$x with spaces? sub('.', ' ', x$x) - removes all letters to the left of each period... Thanks a lot for your advice! -- Dimitri Liakhovitski Ninah.com

Dear all, I want to get the likelihood (or AIC or BIC) of a ridge regression model using lm.ridge from the MASS library. Yet, I can't really find it. As lm.ridge does not return a standard fit object, it doesn't work with functions like e.g. BIC (nlme package). Is there a way around it? I would calculate it myself, but I'm not sure how to do that for a ridge regression. Thank you in

I am not sure if this question has been asked before - but is there a procedure in R (in lm or glm?) that is equivalent to ENTER and REMOVE regression commands in SPSS? Thanks a lot! -- Dimitri Liakhovitski Ninah.com Dimitri.Liakhovitski at ninah.com

Hi, I'm a beginner in the field, I have to perform the ridge regression with lm.ridge for many datasets, and I wanted to do it in an automatic way. In which way I can automatically choose lambda ? As said, right now I'm using lm.ridge MASS function, which I found quite simple and fast, and I've seen that among the returned values there are HKB estimate of the ridge constant and L-W

Dear all, I have a function that predicts DV based on one predictor pred: pred<-c(0,3000000,7800000,15600000,23400000,131200000) DV<-c(0,500,1000,1400,1700,1900) ## I define Function 1 that computes the predicted value based on pred values and parameters a and b: calc_DV_pred <- function(a,b) { DV_pred <- rep(0,(length(pred))) for(i in 1:length(DV_pred)){ DV_pred[i] <- a *

For an application of ridge regression, I need to get the covariance matrices of the estimated regression coefficients in addition to the coefficients for all values of the ridge contstant, lambda. I've studied the code in MASS:::lm.ridge, but don't see how to do this because the code is vectorized using one svd calculation. The relevant lines from lm.ridge, using X, Y are:

Dear R-ers, I have a large data frame (several thousands of rows and about 2.5 thousand columns). One variable ("group") is a grouping variable with over 30 levels. And I have a lot of NAs. For each variable, I need to divide each value by variable mean - by subgroup. I have the code but it's way too slow - takes me about 1.5 hours. Below is a data example and my code that is too

Hello, the code below gives me the picture I need - but there is on small thing I can't figure out. The plot has very small tick mark labels for both axes. I don't mean the axis labels - they are both good, but what is shown near the tick marks. Please help me figure out what parameter I should add to make those larger. I tried sticking cex.lab=1.3 in different places but it didn't